Read duplicates
all_data %>%
select(dataset,Extraction,duplicates,Taxon) %>%
unique() %>%
group_by(Taxon,Extraction) %>%
summarise(value = sprintf("%.1f±%.1f", mean(duplicates), sd(duplicates))) %>%
pivot_wider(names_from = Extraction, values_from = value) %>%
tt(caption = "Mean and standard deviation of fraction of duplicated reads")
tinytable_5mvon2r04tnisfgjj3n9
Mean and standard deviation of fraction of duplicated reads
| Taxon |
ZYMO |
DREX |
EHEX |
| Amphibian |
0.3±0.2 |
0.2±0.2 |
0.2±0.2 |
| Reptile |
0.5±0.4 |
0.3±0.3 |
0.4±0.4 |
| Mammal |
0.4±0.4 |
0.2±0.1 |
0.2±0.2 |
| Bird |
0.8±0.3 |
0.9±0.1 |
0.6±0.4 |
| Control |
1.0±0.0 |
0.9±0.0 |
1.0±0.0 |
all_data %>%
select(dataset,Extraction,duplicates,Taxon, Species) %>%
mutate(duplicates=duplicates*100) %>%
unique() %>%
ggplot(aes(x=Extraction,y=duplicates, color=Species, group=Extraction)) +
scale_y_reverse() +
geom_boxplot(outlier.shape = NA, fill="#f4f4f4", color="#8c8c8c") +
geom_jitter() +
scale_color_manual(values=vertebrate_colors) +
facet_grid(. ~ Taxon, scales = "free") +
theme_minimal() +
labs(y="Duplication rate (%)",x="Extraction method")

all_data %>%
select(dataset,Sample,Species,Extraction,duplicates,Taxon) %>%
filter(Taxon != "Control") %>%
lmerTest::lmer(duplicates ~ Extraction + (1 | Sample) + (1 | Species), data = ., REML = FALSE) %>%
broom.mixed::tidy() %>%
tt()
tinytable_b64lszpfb3xe8cj6wfa5
| effect |
group |
term |
estimate |
std.error |
statistic |
df |
p.value |
| fixed |
NA |
(Intercept) |
0.48640693 |
0.07257915 |
6.701744 |
14.33359 |
8.937456e-06 |
| fixed |
NA |
ExtractionDREX |
-0.09279626 |
0.03900562 |
-2.379048 |
160.72377 |
1.853073e-02 |
| fixed |
NA |
ExtractionEHEX |
-0.14618630 |
0.03918499 |
-3.730671 |
160.73104 |
2.644423e-04 |
| ran_pars |
Sample |
sd__(Intercept) |
0.00000000 |
NA |
NA |
NA |
NA |
| ran_pars |
Species |
sd__(Intercept) |
0.23148121 |
NA |
NA |
NA |
NA |
| ran_pars |
Residual |
sd__Observation |
0.21005171 |
NA |
NA |
NA |
NA |
Depth of coverage
all_data %>%
select(dataset,Extraction,coverage_depth,Taxon) %>%
unique() %>%
group_by(Taxon,Extraction) %>%
summarise(value = sprintf("%.1f±%.1f", mean(coverage_depth), sd(coverage_depth))) %>%
pivot_wider(names_from = Extraction, values_from = value) %>%
tt(caption = "Mean and standard deviation of fraction of duplicated reads")
tinytable_uu6t9qkg0vg7eahm5rup
Mean and standard deviation of fraction of duplicated reads
| Taxon |
ZYMO |
DREX |
EHEX |
| Amphibian |
0.0±0.0 |
0.0±0.0 |
0.0±0.0 |
| Reptile |
0.2±0.3 |
0.1±0.1 |
0.1±0.1 |
| Mammal |
0.7±1.2 |
0.3±0.4 |
0.5±1.0 |
| Bird |
0.4±0.8 |
0.6±0.5 |
0.8±0.8 |
| Control |
0.0±0.0 |
0.0±0.0 |
0.0±0.0 |
all_data %>%
select(dataset,Extraction,coverage_depth,Taxon, Species) %>%
unique() %>%
ggplot(aes(x=Extraction,y=coverage_depth, color=Species, group=Extraction)) +
geom_boxplot(outlier.shape = NA, fill="#f4f4f4", color="#8c8c8c") +
geom_jitter() +
scale_color_manual(values=vertebrate_colors) +
facet_grid(. ~ Taxon, scales = "free") +
theme_minimal() +
labs(y="Depth of coverage",x="Extraction method")

all_data %>%
select(dataset,Sample,Species,Extraction,coverage_depth,Taxon) %>%
unique() %>%
filter(Taxon != "Control") %>%
lmerTest::lmer(coverage_depth ~ Extraction + (1 | Sample) + (1 | Species), data = ., REML = FALSE) %>%
broom.mixed::tidy() %>%
tt()
tinytable_pv8megf2wmkcswl4bmm2
| effect |
group |
term |
estimate |
std.error |
statistic |
df |
p.value |
| fixed |
NA |
(Intercept) |
0.329125000 |
0.1331063 |
2.47264850 |
20.58494 |
0.02222872 |
| fixed |
NA |
ExtractionDREX |
-0.089791667 |
0.1144640 |
-0.78445305 |
48.00000 |
0.43662873 |
| fixed |
NA |
ExtractionEHEX |
-0.002791667 |
0.1144640 |
-0.02438903 |
48.00000 |
0.98064341 |
| ran_pars |
Sample |
sd__(Intercept) |
0.408634577 |
NA |
NA |
NA |
NA |
| ran_pars |
Species |
sd__(Intercept) |
0.224731208 |
NA |
NA |
NA |
NA |
| ran_pars |
Residual |
sd__Observation |
0.396515070 |
NA |
NA |
NA |
NA |
Breadth of coverage
all_data %>%
select(dataset,Extraction,coverage_breadth,Taxon) %>%
unique() %>%
group_by(Taxon,Extraction) %>%
summarise(value = sprintf("%.1f±%.1f", mean(coverage_breadth), sd(coverage_breadth))) %>%
pivot_wider(names_from = Extraction, values_from = value) %>%
tt(caption = "Mean and standard deviation of depth of host genome coverage")
tinytable_a5sije3mccub4l4hgex0
Mean and standard deviation of depth of host genome coverage
| Taxon |
ZYMO |
DREX |
EHEX |
| Amphibian |
0.0±0.0 |
0.0±0.0 |
0.0±0.0 |
| Reptile |
4.9±7.5 |
3.0±5.4 |
2.9±3.7 |
| Mammal |
5.7±5.9 |
10.2±16.4 |
15.1±26.4 |
| Bird |
0.6±0.5 |
3.2±4.4 |
8.9±13.9 |
| Control |
0.0±0.0 |
0.0±0.0 |
0.0±0.0 |
all_data %>%
select(dataset,Extraction,coverage_breadth,Taxon,Species) %>%
unique() %>%
ggplot(aes(x=Extraction,y=coverage_breadth, color=Species, group=Extraction)) +
geom_boxplot(outlier.shape = NA, fill="#f4f4f4", color="#8c8c8c") +
geom_jitter() +
scale_color_manual(values=vertebrate_colors) +
facet_grid(. ~ Taxon, scales = "free") +
theme_minimal() +
labs(y="Breadth of coverage (%)",x="Extraction method")

all_data %>%
select(dataset,Extraction,Sample,Species,coverage_breadth,Taxon) %>%
unique() %>%
filter(Taxon != "Control") %>%
lmerTest::lmer(coverage_breadth ~ Extraction + (1 | Sample) + (1 | Species), data = ., REML = FALSE) %>%
broom.mixed::tidy() %>%
tt()
tinytable_g1wp43v3o9bqvg6g0jfa
| effect |
group |
term |
estimate |
std.error |
statistic |
df |
p.value |
| fixed |
NA |
(Intercept) |
2.799167 |
2.252826 |
1.2425133 |
20.82558 |
0.22785670 |
| fixed |
NA |
ExtractionDREX |
1.301250 |
1.956426 |
0.6651158 |
48.00000 |
0.50915975 |
| fixed |
NA |
ExtractionEHEX |
3.918750 |
1.956426 |
2.0030144 |
48.00000 |
0.05084021 |
| ran_pars |
Sample |
sd__(Intercept) |
7.118462 |
NA |
NA |
NA |
NA |
| ran_pars |
Species |
sd__(Intercept) |
3.549767 |
NA |
NA |
NA |
NA |
| ran_pars |
Residual |
sd__Observation |
6.777259 |
NA |
NA |
NA |
NA |